[PPT] - NMDOT Pavement Management Uses in Meeting Federal Requirements and PowerPoint Presentation

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April 24 and 26, 2018 Shawn Romero, EI Jeff Mann, PE

Pavement Management And Design Bureau

NMDOT Pavement Management – Uses in Meeting Federal Requirements and Project Selection

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Overview – This Is What We are Talking About

2

Generalized Pavement Condition Curve

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Overview

Introduction to and History of Pavement

Management (PMS db) (JSM)

What, When, How of NMDOT PMS db (JSM)
PMS db Data Collection Procedures (SR)
PMS db Data Collection QC/QA Procedures (SR)
PMS db (SR)

– Projections, Scenarios, Budget, Project Selection

23 CFR 490 Requirements and Discussion Points

(JSM)

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Introduction to and History of Pavement Management Systems and Database (PMS db)

Who Knows When it Started
1956-1960 AASHO Road Test

– Ottawa, Illinois – Pavement Serviceability Index (PSI) was born

Concept that “RIDE COMFORT” along w Safety were

the performance objections of ALL PAVEMENTS

1970s

– Several Papers on Pavement Management – 1977 First Textbook “Pavement Management Systems”

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Introduction to and History of Pavement Management Systems and Database (PMS db)

1986 (1993) AASHTO Guide for Design of Pavement Structures

– Chapter 2 Pertains to Network Level and Project Level Determination

23 CFR Part 626 (Non Regulatory)

– Recommends that Pavement Design shall be used in conjunction with performance and cost data from PMS db

MAP 21, FAST Act, 23 CFR 490

– Transportation Asset Mgmt Plan (TAMP) – Require Minimum Standards for Operating PMS db for Interstates and NHS – We will discuss later

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Introduction to and History of Pavement Management Systems and Database (PMS db)

What is a PMS db?

– From 2012, Second Ed “Pavement Management Guide”

“…provides a systematic approach to management a pavement

network that enables agencies (NMDOT) to evaluate the consequences associated with various investment decisions (THINK BUDGETS) and to determine the most cost-effective use of available funds (THINK PERFORMANCE)…

Network Level Data

– ie Performance of our Roadway System Reporting – FHWA HPMS, LFC, 23 CFR 490

Project Level Data

– Managed Section Data of Distress (2 Mile Sections) – NMDOT has ALL distress data for every 1/10 mile of across our network 6

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An Effective Pavement Management System db Should…

Assess Current and Future Pavement Conditions

– Network Level Considerations and Analyze – Performance Curves and Modeling

Estimate Funding Needs to Achieve a Desired Condition

Level

– Budgeting – State of Good Repair

Identify Preservation, Rehabilitation and Reconstruction

Projects that Optimize Funding

– Project Level Analyses

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An Effective Pavement Management System db Should…con’t

Illustrate Consequences of Funding Levels on Condition

– LFC, FHWA, TAMP Reporting – Performing Scenarios – Justify and Defend Funding Levels Compared to Performance

Reporting Methods

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Discussion on Network Level Analyses vs Project Level Analyses

Network Level Analyses

– Consider the Pavement Distress Condition of All Our Roads – Used For Statewide Budgeting – Used for Performance Forecasting – Used for Reporting for Legislative Finance Committee on NM Performance Measures – Used for Transportation Asset Mgmt to meet FHWA Requirements – Composite Index Typically Used – Used for District Budgeting

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Discussion on Network Level Analyses vs Project Level Analyses

Project Level Analyses

– 2 Mile Sections Consider Prevalent Distress and Suggest Recommendation – Based on Decision Trees and Performance Curves and Cost:Benefit

Simply if Roadway has this types of distress or is this

age, then X recommendation

Supplement w coring, field exploration, GPR, FWD

– PavementME or MEPDG Input Data

Calibration of Pavement Distress Models
Materials Database
Traffic Database

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Basics of Pavement Management Systems and Database (PMS db)

Inventory – What Is Important?

– Pavement Condition Data – Roadway Segments, MP – Linear Referencing System (LRS) – Functional Classification – Pavement Section, Type – Shoulder Information – Number of Lanes – Construction History (1,900 Records)

Integration with MMS

– Traffic Data and WIM Data – Materials Related Data – Cost Data

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Pavement Condition Assessment

Types of Pavement Condition Data Collected

– Distresses (FHWA LTPP Guide, 2014) – Structural Capacity

FWD?
RWD?
Traffic Speed Deflectomer?

– Surface Characteristics

Friction?
Noise?
Techniques for Data Collection

– Manual – Semi-Manual (NDT??) – Fully Automatic

NMDOT Since 2013 Moved to Fully Automatic

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Pavement Condition Assessment

Applicable AASHTO Standards

– R48: Standard Practice for Determining Rut Depth in Pavements – R36: Standard Practice for Evaluating Faulting of Concrete Pavements – R55: Standard Practice of Quantifying Cracks in Asphalt Pavement Surface – R43: Standard Practice for Quantifying Roughness of Pavements (IRI)

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NMDOT PMS db Distresses

Based on Long Term Pavement Performance LTPP FHWA Guidance

– NMDOT Measures and Determines the Severity and Extent of Following Distresses

Raveling and Weathering
Bleeding
Transverse Cracking
Alligator Cracks
Edge Cracks
Longitudinal Cracks
Patching
Block Cracking
IRI and Rutting

…and concrete pavement distress too

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Developing Pavement Condition Indices

What are Pavement Condition Indices?

– Typically a numerical index between 0 to 100 which is used to indicate condition of pavement.

NMDOT use PCR (Pavement Condition Rating)

– Composite Index

– Subcategories

Composite Index
Individual Index

– Composite Index

PCI, PCR, PSI

– Individual Index

NMDOT uses Structural Index, Environmental, Safety Index,

Roughness Index

– Evaluates Distress for Each Individual Index – Used for Decision Trees 15

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NMDOT PMS db History of Implementation

1990’s to 2006 (Fuzzy)

– Districts Provided Assistance on Pavement Distress Data Collection

2006

– NMSU and UNM Provided Manual Data Collection – PMS db Begins

2012-2013

– Steering Committee Formed w District and General Office Representation and KEI Engineering Hired to Help w Configuration – Summer 2013 Executive Decision to move to Automated Distress Data Collection Methods – Requiring New Configuration of Agile PMS db

2013-2017

– Moved to Automated, Mandli Data Collection – Reconfiguration

2018

– Fugro – Reconfiguration Planned for Performance Curves based on Construction History and Maintenance History data

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The NMDOT Pavement Management System db Can…

Assess Current and Future Pavement Conditions

– Network Level Considerations and Analyze – Performance Curves and Modeling

Estimate Funding Needs to Achieve a Desired Condition

Level

– Budgeting – State of Good Repair

Identify Preservation, Rehabilitation and Reconstruction

Projects that Optimize Funding

– Project Level Analyses

Illustrate Consequences of Funding Levels on Condition

– LFC, FHWA, TAMP Reporting – Performing Scenarios – Justify and Defend Funding Levels Compared to Performance

Reporting Methods

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PMS Data Collection Procedures

What type of distress are being collected
How is the data being collected
What control procedures are in place
Why are we collecting this information
How is this data being used

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Data History

Prior to automated collection NMDOT would collect IRI in house and contract a University to manual/visual survey and collect distress.

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Data definitions and some practices developed during manual survey were carried over to automated data collection

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Data History

New Mexico’s pavement distress definitions and collection methods were derived from FHWA’s Distress Identification and HPMS Manual along with data collection practices carried on from district distress field survey

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Define Pavement Distress

Alligator Cracks: Pattern of interconnected cracks resembling chicken wire or alligator

skin. Longitudinal cracks in the

wheel path are rated as Low severity alligator cracking. Severities 2 and 3 must have at least 3 cells.

1.

Low: Hairline, disconnected cracks, 1/8-inch wide or less, less than 3 cells. No spalls.AND/OR a longitudinal crack, any severity, in the wheel path. 2. Medium: Fully developed cracks greater than 1/8-inch

wide. Three or more cells. Lightly spalled.

3. High: Severely spalled, cells rock, and may pump. PACE OFF the cumulative lengths of EACH severity

present. Record lengths (in

paces). Mark location of

ccurrence in field form: 1
r 2 wheel paths.

Edge Cracks: Cracks that lie within 1 foot of the edge line. Does NOT apply in roads with curb and gutter installations.

1.

Low: Less than ¼-inch wide. No spalls. 2. Med: Greater than ¼-inch wide. Some spalling may be present, but pavement is still intact. 3. High: Severely spalled. Pieces of pavement have broken

ff the edge of the roadway.

1.

Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more. Longitudinal Cracks: ANY longitudinal crack NOT in the wheel path, but NOT within 1’ of the edge line.

1.

Low: Unsealed, mean width of less than ¼-inch. OR sealed with sealant in good condition, any width. 2. Medium:Any crack with average width greater than ¼- inch and less than ¾ inch. May have adjacent Low severity random cracks and some spalling. 3. High: Any crack wider than ¾ inch, may have adjacent moderate to high random cracking and spalling. 1. Low: 1% to 30% of sample section. 2. Medium: 31% to 60% of sample section. 3. High: 61% or more of sample section. Patching: Any new pavement placed into the pavement section. Extent is rated as percent of the test section affected.

1.

Low: Patch is in good condition.

2.

Medium: Somewhat deteriorated, has Low to Medium severities of any distress present.

3. High: Needs replacement. High Severity of any distress,

gaps are present between the pavement and the patch. 1. Low: 1% to 30%

2. Med: 31% to 60%
3. High: 61% or more

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Automated Data Collection

2013 The Department contracted with Mandli Communications to collect fully automated distress data, a 4 year contract.

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2018 a new vendor (Fugro) was selected to collect distress data for the next for years.

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Data Collection

15,000 Lane Miles each year, including:
100% NHS routes
50% Non-NHS routes
Pavement Distress Indices:
IRI
Rutting
Faulting
Raveling
Bleeding
Patching
Cracking
Corner Break
Joint Count
Joint Seal Damage
Joint Crack Spalling
Photologs
Lidar and Assets Collected in 2013

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How is the Data Collected

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Data Collection

Rutting

Laser Crack Measurement System (LCMS) Collect Left, Right and Average Rut Depth (in)

Faulting

Laser Crack Measurement System (LCMS) Average (in)

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Severity

1. Low: Faulted joints or cracks which average 1/16-inch or less. 2. Med: Faulted joints or cracks which average more than 1/16-inch; but less than 1/4-inch. 3. High: Faulted joints or cracks which average 1/4-inch or more.

Extent

1. Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more.

(27Sev low+1Sev med)/34joint count)(100)= 82%= High Extent (3)

Extent Severity

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Data Collection

Cracking

Laser Crack Measurement System (LCMS)

Collect: -HPMS Percent –Length –Longitudinal –Block –Fatigue - Corner -Edge

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Severity

1. Low: Unsealed, mean width of less than ¼-inch. OR sealed with sealant in good condition, any width. 2. Medium: Any crack with average width greater than ¼-inch and less than ¾-inch. May have adjacent Low severity random cracks and some spalling. 3. High: Any crack wider than ¾-inch, may have adjacent moderate to high random cracking and spalling

Extent

1. Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more.

Longitudinal Cracks:

ANY longitudinal crack NOT in the wheel path, but NOT within 1’ of the pavement white line.

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Data Collection

HPMS Cracking 30” Wheel path to 39”(2017 Collection)

AASHTO Designation: PP 67-14161 Release: Group 1(April 2016)

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Data Collection

International Roughness Index (IRI) Dynatest MK-IV Road Surface Profiler Collect Left, Right and Average IRI (In/Mile)

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40. 60. 80. 100. 120. 140. 160. 180. 200. 220. 240. 260. 280. 30.7 30.9 31.1 31.3 31.5 31.7 31.9 32.1 32.3 32.5 32.7 32.9 33.1 33.3 33.5 33.7 33.9 34.1 34.3 34.5 34.7 34.9 Inches/Mile Milepoint

US-60 Eastbound MP 30.7-34.8 IRI

Average Left Right

Good Fair Poor

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Example: Downward facing view of roadway

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Example: Lidar

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Data Quality Assurance/Control

Contractor Quality Control (QC) Pre-Deployment

Vehicle Configuration
Collection vehicle to meet criteria specified by NMDOT
Laser Crack Measurement System (LCMS) configuration
Positional Orientation System (POS) configuration
Camera Setup
System Certification
Ten 0.1 mile runs of IRI data have less than 5% standard deviation from the mean
Internally created procedure taking components from AASHTO R-56-14
10 runs of LCMS data have a minimum repeatability of 92% compared to profile

created with a SurPRO (ProVAL Certification Module)

Compares well to historical data from the same course

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Data Quality Assurance/Control

Contractor Quality Control (QC)

Daily Checks

Bounce test values do not exceed 8 in./mile (elements from AASHTO R-57-

14)

LCMS Static Validation performed on a weekly basis
LCMS height detection is comparable to previous days reading
Ambient temperature is within system operating ranges of (>32ºF <104ºF)
Imagery is in focus, color is appropriate, and is of acceptable quality
Monitor the GPS satellite coverage, along with reported accuracies
Monitor the photolog and downward imagery for quality and lane

placement

Use a mapping program to determine completeness of collection

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Data Quality Assurance/Control

Contractor Quality Control (QC)

Validation Sites

Validation courses have been strategically located to aid in the efficiency of

collection, and confirm the systems are functioning in multiple scenarios

Analyze and validated by third party independent firm

– Report right wheel path standard deviation from data collection vehicles initial validations compared to final, for each deployed vehicle – Report left wheel path standard deviation from data collection vehicles initial validations compared to final, for each deployed vehicle – Report right rutting average variance from data collection vehicles initial validations compared to final – Report left rutting average variance from data collection vehicles initial validations compared to final

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Data Quality Assurance/Control

Contractor Quality Control (QC)

Data Reduction

Data reduction team is properly trained and tested on manual distress

classifications and rating rules prior to rating of production data

Check LCMS data for null rutting values, invalid rutting values, outside

acceptable temperatures

Distress production data is reviewed to assure rating understanding

remains consistent through the course of the project

Data Delivery

Roadway conditions outliers are performed on aggregated 1 mile

segments

Max and Min Values
IRI Max and Min Values (30>IRI>400), differ by more than 50 in/mile.
Rutting Max Values greater than 0.35 in, differ by more than .25in
Faulting Max Values greater than 1in.
Roadway Geometric Outliers
Absolute Values exceeding 8%
Absolute grade values greater than 12%
Absolute Curve values greater than 100 degrees
No more than 10 consecutive fixed segments will be missing data (500ft)

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Data Quality Assurance/Control

Department Quality Assurance (QA)

New Mexico DOT receives data from the Data Collection Contractor on a monthly basis and conducts a review up to 10% of the submitted data and reports any inconsistences to the Data Collection Contractor’s Project Manager for action (i.e., correction, re-collection).

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Data Quality Assurance/Control

Data Delivery Checks

Total network miles (excludes areas closed to

construction)

Delivered data accurately populated with description

information (system, route, direction, and begin and end latitude/longitude

Photo Images are clear and aligned
For 10th mile segments

– Raveling values must add up to 528 feet. – Maximum bleeding can be 528 feet. – Maximum fatigue (alligator) cracking can be 6336 square feet. – Maximum longitudinal cracking can be 1584 linear feet. – Maximum edge cracking can be 528 feet. – Maximum block cracking can be 6336 square feet.

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Data Quality Assurance/Control

Department Quality Assurance (QA) Contractor resolution

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National Performance Management Measures; Assessing Pavement Condition for the National Highway Performance Program and Bridge Condition for the National Highway Performance Program

23 CFR Part 490

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§ 490.319 Other requirements.

(2) Not later than 1 year after the effective date of this regulation, State DOTs shall submit their Data Quality Management Program to FHWA for approval.

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Practical Guide for Quality Management

f Pavement Condition Data Collection

“This guide outlines a process for systematically implementing QM practices throughout the data collection effort. It describes the roles and responsibilities for successful QM of the data and presents the practices currently in use by transportation agencies.”

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Linda Pierce

Revising Data Quality Management Plan for FHWA’s Acceptance

Data Quality Management Plan

Current QA Practices 23 CFR Part 490 FHWA Comments NMDOT’s DQMP Draft

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Revising Data Quality Management Plan for FHWA’s Acceptance

§ 490.319 Other requirements.

(1) In a Data Quality Management Programs, State DOTs shall include, at a minimum, methods and processes for: (i) Data collection equipment calibration and certification; (ii) Certification process for persons performing manual data collection; (iii) Data quality control measures to be conducted before data collection begins and periodically during the data collection program; (iv) Data sampling, review and checking processes; and (v) Error resolution procedures and data acceptance criteria.

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Additions to DQMP

Training Certifications
Vendor Dispute Resolutions
Ground Truth

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Moving forward with DQMP

Supporting document to the DQMP that includes

Pavement definitions and descriptions
Field collection procedures
Guidelines to rate sections based on digital images
Certification test

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1. Providing a network view of pavement

conditions

Reporting conditions to legislators and

federal government

2. Assisting in determining future projects
3. Supports funding request with data and

modeled predictions

What we are Doing With the Data

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Federal Reporting

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23 CFR Part 490 National Performance Measures

Currently do not have measures set up in the pavement

management system

Calculation of overall condition pavement condition with requirements of

SS490.309(b)

All Pavements Good < 5 < 5 < 5 0.00 < 0.20 0.00 < 0.10

95

Good Fair 5

15

5

10

5

20

0.20

0.40

0.10

0.15

96

170 Fair

Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor Rigid

Rating Rating Cracking (%) Rutting (Inches) Cracking (%) Cracking (%)

JCP CRCP Flexible

Faulting (Inches) IRI (in/mile)

Flexible

TOTAL 3390.12 3451.98 207.57 Route Lane Begin Mile End Mile Length Lane Miles HPMS Cracking Percent Average IRI Rutting Measure Overall Pavement Condtion Measure GOOD FAIR POOR BL-11-P All 0.00 0.10 0.10 0.20 9.00 FAIR 122. FAIR 0.14 GOOD FAIR 0.2 BL-11-P All 0.10 0.20 0.10 0.20 0.50 GOOD 226. POOR 0.13 GOOD FAIR 0.2 BL-11-P All 0.20 0.30 0.10 0.20 2.80 GOOD 132. FAIR 0.10 GOOD FAIR 0.2 BL-11-P All 0.30 0.40 0.10 0.40 0.00 GOOD 86. GOOD 0.11 GOOD GOOD 0.4 BL-11-P All 0.40 0.50 0.10 0.40 0.00 GOOD 64. GOOD 0.10 GOOD GOOD 0.4 BL-11-P All 0.50 0.60 0.10 0.40 0.10 GOOD 69. GOOD 0.12 GOOD GOOD 0.4 BL-11-P All 0.60 0.70 0.10 0.40 0.00 GOOD 63. GOOD 0.13 GOOD GOOD 0.4 BL-11-P All 0.70 0.80 0.10 0.40 0.10 GOOD 51. GOOD 0.15 GOOD GOOD 0.4 BL-11-P All 0.80 0.90 0.10 0.40 0.00 GOOD 52. GOOD 0.14 GOOD GOOD 0.4 BL-12-P All 0.20 0.30 0.10 0.20 20.70 POOR 95. FAIR 0.26 FAIR FAIR 0.2 BL-12-P All 0.30 0.40 0.10 0.20 48.30 POOR 108. FAIR 0.17 GOOD FAIR 0.2 BL-12-P All 0.40 0.50 0.10 0.20 57.70 POOR 89. GOOD 0.16 GOOD FAIR 0.2 BL-12-P All 0.50 0.60 0.10 0.20 58.20 POOR 129. FAIR 0.20 FAIR FAIR 0.2 BL-12-P All 0.60 0.70 0.10 0.20 45.50 POOR 118. FAIR 0.22 FAIR FAIR 0.2 BL-12-P All 0.70 0.80 0.10 0.20 23.30 POOR 97. FAIR 0.21 FAIR FAIR 0.2 BL-12-P All 0.80 0.90 0.10 0.20 8.40 FAIR 243. POOR 0.20 FAIR FAIR 0.2 BL-12-P All 0.90 1.00 0.10 0.20 13.30 FAIR 133. FAIR 0.19 GOOD FAIR 0.2 BL-12-P All 1.00 1.10 0.10 0.20 20.30 POOR 232. POOR 0.30 FAIR POOR 0.2 BL-12-P All 1.10 1.20 0.10 0.20 15.00 FAIR 163. FAIR 0.24 FAIR FAIR 0.2 7049.68 TOTAL

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Federal Reporting

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State Reporting

Pavement Condition Rating

Pavement Condition Rating 2016 2015 2014 Lane Miles

Good (PCR>=65.5)

Fair (65.5<PCR<=45.5)

Poor (PCR<45.5) Lane Miles Good (PCR>=65.5)

Fair (65.5<PCR<=45.5)

Poor (PCR<45.5) Lane Miles Good (PCR>=65.5)

Fair (65.5<PCR<=45.5)

Poor (PCR<45.5)

Systemwide 31,007 9,388 30% 15,765 51% 5,854 19% 30,965 10,414 34% 14,920 48% 5,631 18% 30,770 10,147 33% 15,452 50% 5,171 17% NHS 12,050 5,041 42% 5,534 46% 1,356 11% 12,050 5,641 47% 4,987 41% 1,280 11% 12,050 5,605 47% 5,278 44% 1,021 8% Non-NHS 19,075 4,347 23% 10,231 54% 4,497 24% 19,057 4,773 25% 9,933 52% 4,352 23% 18,865 4,543 24% 10,173 54% 4,149 22% Interstate 4,108 2,297 56% 1,515 37% 296 7% 4,105 2,344 57% 1,452 35% 310 8% 4,105 2,393 58% 1,490 36% 223 5% Non-Interstate 26,899 7,092 26% 14,250 53% 5,557 21% 26,860 8,070 30% 13,467 50% 5,322 20% 26,664 7,755 29% 13,961 52% 4,948 19% Non-NHS Non Interstate 19,075 4,347 23% 10,231 54% 4,497 24% 19,057 4,773 25% 9,933 52% 4,352 23% 18,865 4,543 24% 10,173 54% 4,149 22%

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Pavement Management System

A Pavement Management System (PMS) is designed to provide objective information and useful data for analysis so that road managers can make more consistent, cost-effective, and defensible decisions related to the preservation

f a pavement network.

While a PMS cannot make final decisions, it can provide the basis for an informed understanding

f the possible consequences of alternative

decisions.

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Data Standardization Pavement Management System

Levels the playing field by converting each distress

value to a 0-100 scale

Overall Condition Index (OCI) is calculated by

combining distress indices

Does not include Roughness IRI

Worst No distress

10 20 30 40 50 60 70 80 90 100

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Pavement Management System Unconstrained Needs

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Route Begin Mile End Mile Maintenance District Mainline Treatment Mainline Treatment Cost ating (PCR) Average IRI Rutting Measureng Percent I-25-M 245.51 247.51 3 - ALBUQUERQUE F4 - Preservation (Major) $390,029.20 73.39 38.19 0.12 0.39 I-25-M 275.13 277.13 5 - SANTA FE F4 - Preservation (Major) $390,029.20 47.43 64.38 0.14 8.07 I-25-M 300.47 302.47 4 - LAS VEGAS F4 - Preservation (Major) $460,943.60 63.40 57.9 0.13 1.01 I-25-M 304.47 306.47 4 - LAS VEGAS F4 - Preservation (Major) $460,943.60 68.11 62.14 0.19 0.83 NM-478-M 22.72 22.88 1 - DEMING F3 - Preservation (Minor) $8,307.75 54.57 235. 0.13 5.29 BL-36-P 2.20 2.36 4 - LAS VEGAS F3 - Preservation (Minor) $9,818.25 43.11 82. 0.16 15.15 NM-37-P 14.00 14.16 2 - ROSWELL F3 - Preservation (Minor) $9,234.00 56.67 184. 0.19 0.00 NM-38-P 29.10 29.27 4 - LAS VEGAS F3 - Preservation (Minor) $10,003.50 53.94 301.5 0.18 1.53 US-54-P 304.91 305.07 4 - LAS VEGAS F3 - Preservation (Minor) $5,094.38 42.24 149. 0.20 9.80 I-25-P 221.57 221.77 3 - ALBUQUERQUE F3 - Preservation (Minor) $10,293.25 58.60 43. 0.13 0.42 I-25-M 221.57 221.77 3 - ALBUQUERQUE F3 - Preservation (Minor) $10,293.25 56.81 37.25 0.11 0.94 NM-314-P 18.33 18.52 3 - ALBUQUERQUE F3 - Preservation (Minor) $10,345.50 66.18 99.5 0.05 1.31 NM-14-P 49.13 49.33 5 - SANTA FE F3 - Preservation (Minor) $20,691.00 73.47 142. 0.25 3.51 NM-14-M 49.13 49.33 5 - SANTA FE F3 - Preservation (Minor) $20,691.00 73.89 143.67 0.19 2.37 BL-13-M 6.30 6.50 3 - ALBUQUERQUE F3 - Preservation (Minor) $10,345.50 62.26 71.5 0.10 3.52 US-60-P 376.93 377.13 2 - ROSWELL F3 - Preservation (Minor) $11,343.00 50.72 169.67 0.23 21.92 NM-268-P 0.00 0.20 2 - ROSWELL F3 - Preservation (Minor) $11,400.00 57.52 102.33 0.16 4.10

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AgileAssets Pavement System

Performance Prediction

Collected pavement distress data is stored in the pavement management system Individual Distress Indices are combined to structural, Environmental, Safety, Roughness and Overall Condition Index

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Performance Prediction

Treatment and Condition Improvement Rules

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Pavement Management System Performance Models

Deterioration Curve Piecewise linear function

Performance Prediction

SLIDE 54

Decisions are driven by cost benefit Exclusion Years are used

Performance Prediction

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PMS makes pavement treatment selections based on decision trees

PMS Logic

Monitor •Preventative •Patch •Preservation (Minor) •Preservation

(Major) •Rehabilitation (Minor) •Rehabilitation (Major) •Reconstruction

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PMS Scenarios

Multi Constraint Optimization - $200M/Yr for 10 Years

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PMS Scenarios - Reporting

Multi Constraint Optimization OCI and treatment cost total

Preservation Rehabilitation

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Project Selection

After a scenario is ran the PMS out puts a list of projects to be considered

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Pavement Condition Reports

Recommendations should consider:

PCR Value
Individual Distresses
Field Exploration (Core and Core Hole

evaluation)

Ground Penetrating Radar (GPR) survey
Field Evaluation

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Reporting Pavement Condition

Crac acking (% (%) Rutting ( g (Inche hes) IRI RI (in in/mile) Ra Ratin ing < 5 0.00 < 0.20

95

Good 5

10

0.20

0.40

96

170

Fair 10 < 0.40 < 170 < Poor

FHWA guidelines in 23 CFR Part 490 set new thresholds for determining pavement conditions

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Pavement Condition Reports

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Pavement Condition Reports

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Pavement Condition Report

Core evaluation influences pavement recommendations

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Pavement Condition Report

GPR evaluation influences pavement recommendations

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1. Data driven pavement location and

treatment decisions, in lieu of funding based or worst first pavement recommendations

2. Summary of the roadway condition

which include all topical distresses, core and bore information as well as GPR infomation

3. An ultimate scoping tool to make the

best project decisions

Condition Report Provides

SLIDE 66

Three main approaches to target- setting using a PMS

APPROACH (SCALE) KEYS TO GETTING IT RIGHT Program (network) level

Confidence in relationship between program

actions and reported performance

Sufficient data to support relationship
Responding to changes in treatments/policies

Project (section) level

Accurate reflection of investments
Performance prediction models for sections
Solving the aggregation problem

0.10-mile (reporting interval) level

Applying performance prediction models and

condition resets at the project level to 0.10-mile segments

66

SLIDE 67

23 CFR 490

To Establish National Performance

Management Measures

– PAVEMENT, Safety, Bridge, Congestion, ect – For Pavement – Established May 20, 2017

MAP-21 and FAST ACT

– Require Transportation Asset Mgmt Plan (TAMP)

67

SLIDE 68

23 CFR 490

Summary: …purpose of this rule…

– Establish measures for State departments of transportation to use to carry out the National Highway Performance Program (NHPP)… – Assess the condition of Pavements on the National Highway System (NHS)…pavements on the Interstate System… – Ensure that investments of Federal-aid funds…are directed to support progress toward the achievement of PERFORMANCE TARGETS established in a State’s asset management plan for NHS. – Establishes regulations that address measures, targets, and reporting

68

SLIDE 69

23 CFR 490.105 Establishment of Performance Targets

69

THIS IS WHY WE ARE HERE

State DOT and MPO shall establish

performance targets for all measures for condition of pavements on Interstate System and Non-Interstate NHS

State DOT shall establish target 1 year

from effective date of rule (by May 20, 2018)

State DOT shall COORDINATE with

relevant MPO on selection of targets

SLIDE 70

23 CFR 490.105 Establishment of Performance Targets

70

THIS IS WHY WE ARE HERE

State DOT shall provide Baseline

Performance Period Report where performance period is 4 years

– Require a 2-year Target – Require a 4-year Target

Reporting

– State DOT shall report Baseline, 2- and 4-year targets and the basis for established targets (PMS and Budget Allocation) – State DOT shall provide relevant MPO targets to FHWA

SLIDE 71

23 CFR 490.105 Establishment of Performance Targets

71

THIS IS WHY WE ARE HERE

MPO shall establish targets for each

performance measure

MPO shall establish targets no later

than 180 days after State DOT target established

MPO shall establish 4-year target

SLIDE 72

23 CFR 490.105 Establishment of Performance Targets

72

CRITICAL POINT

MPO has option to agreeing to plan

and program projects so that they contribute toward the accomplishment

f relevant State DOT target

OR

Committing to a quantifiable target for

that performance measure for their metro planning area

SLIDE 73

23 CFR 490.307 Pavement Performance (Distress) Metrics

73

Based on (current) HPMS Field Manual method of

collection and calculation

1/10 Mile Data
Asphalt Pavements

– IRI (PSR), Rutting, % Cracking

Jointed Concrete Pavements

– IRI (PSR), Faulting, % Cracking

Continuously Reinforced Concrete Pavements

– IRI (PSR), % Cracking

NMDOT Pavement Data Collection Cycle

– Yearly NHS and Interstate – Every Other Year Non-NHS (full collection every 2 years) – 2013-2017 Previous Vendor – New Contract 2018-2021

SLIDE 74

23 CFR 490.313 Calculation of Pavement Performance (Distress) Measures

74

Pavement Measures (IRI, % Cracking, ect) from 490.307 Shall

Be Calculated…used by State DOT and MPO to carry out pavement condition related requirements

Performance Measures is good, fair and poor based on

following rating (criteria)

More than 2 Performance Measures are Poor – Roadway

Segment is Classified as poor

All Pavements Good < 5 < 5 < 5 0.00 < 0.20 0.00 < 0.10

95 Good

Fair 5

15

5

10

5

20

0.20

0.40 0.10
0.15

96

170 Fair

Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor Rigid

Rating Rating Cracking (%) Rutting (Inches) Cracking (%) Cracking (%)

JCP CRCP Flexible

Faulting (Inches) IRI (in/mile)

Flexible

SLIDE 75

75

23 CFR Part 490 10th Mile Thresholds

All Pavements Good < 5 < 5 < 5 0.00 < 0.20 0.00 < 0.10

95

Good Fair 5

15

5

10

5

20

0.20

0.40

0.10

0.15

96

170 Fair

Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor Rigid

Rating Rating Cracking (%) Rutting (Inches) Cracking (%) Cracking (%)

JCP CRCP Flexible

Faulting (Inches) IRI (in/mile)

Flexible

Year Route Lane Begin Mile End Mile Length Lane Miles Overall Pavement Condition 2015 BL-11-P All 0.00 0.10 0.10 0.20 9.00 FAIR 122. FAIR 0.14 GOOD FAIR 2015 BL-11-P All 0.10 0.20 0.10 0.20 0.50 GOOD 226. POOR 0.13 GOOD FAIR 2015 BL-11-P All 0.20 0.30 0.10 0.20 2.80 GOOD 132. FAIR 0.10 GOOD FAIR 2015 BL-11-P All 0.30 0.40 0.10 0.40 0.00 GOOD 86. GOOD 0.11 GOOD GOOD 2015 BL-11-P All 0.40 0.50 0.10 0.40 0.00 GOOD 64. GOOD 0.10 GOOD GOOD 2015 BL-11-P All 0.50 0.60 0.10 0.40 0.10 GOOD 69. GOOD 0.12 GOOD GOOD 2015 BL-11-P All 0.60 0.70 0.10 0.40 0.00 GOOD 63. GOOD 0.13 GOOD GOOD 2015 BL-11-P All 0.70 0.80 0.10 0.40 0.10 GOOD 51. GOOD 0.15 GOOD GOOD 2015 BL-11-P All 0.80 0.90 0.10 0.40 0.00 GOOD 52. GOOD 0.14 GOOD GOOD 2015 BL-12-P All 0.20 0.30 0.10 0.20 20.70 POOR 95. FAIR 0.26 FAIR FAIR 2015 BL-12-P All 0.30 0.40 0.10 0.20 48.30 POOR 108. FAIR 0.17 GOOD FAIR 2015 BL-12-P All 0.40 0.50 0.10 0.20 57.70 POOR 89. GOOD 0.16 GOOD FAIR 2015 BL-12-P All 0.50 0.60 0.10 0.20 58.20 POOR 129. FAIR 0.20 FAIR FAIR 2015 BL-12-P All 0.60 0.70 0.10 0.20 45.50 POOR 118. FAIR 0.22 FAIR FAIR 2015 BL-12-P All 0.70 0.80 0.10 0.20 23.30 POOR 97. FAIR 0.21 FAIR FAIR 2015 BL-12-P All 0.80 0.90 0.10 0.20 8.40 FAIR 243. POOR 0.20 FAIR FAIR 2015 BL-12-P All 0.90 1.00 0.10 0.20 13.30 FAIR 133. FAIR 0.19 GOOD FAIR 2015 BL-12-P All 1.00 1.10 0.10 0.20 20.30 POOR 232. POOR 0.30 FAIR POOR 2015 BL-12-P All 1.10 1.20 0.10 0.20 15.00 FAIR 163. FAIR 0.24 FAIR FAIR HPMS Cracking Percent Average IRI Rutting Measure

Overall Condition: Good: All three ratings are Good Poor: Two or more ratings are Poor Fair: Does not meet Good or Poor Condition

SLIDE 76

23 CFR 490.315 Pavement – Minimal Level of Condition (LOC)

76

Minimal LOC of Interstate NHS

– …percentage of lane-miles of Interstate System in Poor condition…shall not exceed 5.0 percent – NMDOT 2017 Current Condition of Interstate is <1% Poor

SLIDE 77

23 CFR 490.315 Pavement – Minimal Level of Condition

77

Minimal LOC – Non-Interstate NHS

– CFR Does NOT REQUIRE Minimum LOC for Non-Interstate NHS – NMDOT 2017 Current Condition of Non-Interstate NHS is 5.9% Poor

SLIDE 78

NMDOT NHS Current and Projected Condition Rating

78

Interstate

SLIDE 79

NMDOT NHS Current and Projected Condition Rating

79

Non-Interstate NHS

SLIDE 80

NMDOT NHS Pavements and Bridges Federal 2 &4 Year Projected Targets

80

Performance Measure 2 Year (2019) 4 Year (2021) Percentage of Bridges on the NHS in Good condition 36.0% 30.0% Percentage of Bridges on the NHS in Poor condition 3.3% 2.5% Percentage of Interstate pavement on the NHS in Good condition 57.3% 59.1% Percentage of Interstate pavement on the NHS in Poor condition 4.5% 6.3% Percentage of Non-Interstate pavement on the NHS in Good Condition 35.6% 34.2% Percentage of Non-Interstate pavement on the NHS in Poor Condition 9.0% 12.0%

SLIDE 81

81